We found Google's T5 models which were released in 2019, pre-GPT-3, were "secretly" capable of in-context learning with a simple inference technique.
Given they use a bidirectional MLM (Masked Language Modeling) objective, it wasn't obvious how to do it, but MLM objectives are known to produce better language representations than causal (next token prediction) objectives. We were able to outperform much larger sized GPT-3 models or get very close to their performance with far smaller T5 models.
Are there any intrinsic dis/advantages of bidirectional models over causal models for in-context learning? It seems that unidirectional model just have been explored and worked on more.
When you train bidirectionally only, you don't get a generative model, that would be the downside. However, you can train on a mixture of causal and bidirectional objectives as some LLM pre-training has done. As far as I am aware, there are no downsides of that, but it is not more common simply because the standard practice has been to train causal only and there just isn't enough funding/attention to go into experimenting on every axis of pre-training (which can be very expensive).
It's not at all expected. T5 models are not generative models by default and they were not thought to be able to perform generation, let alone in-context learning. Remember these models were released before any of the existing LLMs and in-context learning/prompting as a technique became popularized with GPT-3.
While the technique requires multiple samples to coax generations from this particular model, other LLM training schemes have incorporated both unidirectional and bidirectional objectives in their training now. However, this exploration hasn't been fully resolved as most models are still trained only on the causal objective by standard practice. There's still a a lot of exploration that can be done on pre-training objectives.
The "embarrassingly simple inference technique" is to put a bunch of [MASK] tokens at the end of the prompt.
I'm having trouble understanding whether this paper is saying anything new. The original BERT paper already compared it favourably to causal models including GPT. Was there any doubt that BERT-style models could be in-context learners?
From what I gather as a non-expert, the problem with BERT is scaling/training efficiency: GPT gets C-1 training examples out of a training input of length C, but BERT only gets 0.15*C examples. Indeed, the author points out that DeBERTa required 3x more compute than GPT-3 to achieve the level of performance reported, which makes sense.
As someone who has very limited understanding but tried to use BERT for classification, is BERT still relavant when compared to LLMs ? Asking because I hardly see any mention of BERTs anymore.
- Encoder based models have much faster inference (are auto-regressive) and are smaller. They are great for applications where speed and efficiency are key.
- Most embedding models are BERT-based (see MTEB leaderboard). So widely used for retrieval.
- They are also used to filter data for pre-training decoder models. The Llama 3 authors used a quality classifier (DistilRoberta) to generate quality scores for documents. Something similar is done for FineWeb Edu
Wait, I thought GPT's were autoregressive and encoder only like BERT used masked tokens? You're saying BERT is auto-regressive or am I misunderstanding?
You're right. Encoder only models like BERT aren't auto-regressive and are trained with the MLM objective. Decoder only (GPT) and encoder-decoder (T5) models are auto-regressive and are trained with the CLM and sometimes the PrefixLM objectives.
They're still very useful on their own. But even more broadly, you can often use them in tandem with LLMs. A good example could be a classifier that's used as a "router" of sorts; could be for selecting a prompt template, directing to a specific model, or loading a LoRA or soft prompt vector to be used at inference-time.
For many specialized tasks you can run BERTs (and simpler models in general) at scale, with lower latency, at lesser cost, with similar or even better results.
Depends what you’re trying to do. I’m writing a personal assistant app (speech to text) and want to classify the user input according to the current actions I support (or don’t). The flagship LLMs are pretty great at it if you include the classes in the prompt and they will spit out structured output every time. But, man, they are expensive and there’s the privacy aspect I’d prefer to adhere to. I’ve only got 24 GB of RAM, so I can’t run too many fancy local models and things like llama3.1:8b don’t classify very well.
They’ve drowned in the LLM noise, but they’re definitely still relevant.
- Generative model outputs are not always desirable, and often even undesirable
- BERT models are smaller and can run with lower latency and serve larger batches with lower vram requirements
- BERT models have bidirectional attention, which can improve performance in many applications
LLMs are “cheap” in the sense that they work well generically, without requiring fine tuning. Where they overlap with BERT models is mostly that they may work better in low training data environments due to better generalization capabilities.
But mostly companies like them because they don’t “require” ML engineers or data scientists on staff. For the lack of care given to evaluation that I see around LLM apps, I suspect that’s going to prove to be a faulty premise.
We found Google's T5 models which were released in 2019, pre-GPT-3, were "secretly" capable of in-context learning with a simple inference technique.
Given they use a bidirectional MLM (Masked Language Modeling) objective, it wasn't obvious how to do it, but MLM objectives are known to produce better language representations than causal (next token prediction) objectives. We were able to outperform much larger sized GPT-3 models or get very close to their performance with far smaller T5 models.
While the technique requires multiple samples to coax generations from this particular model, other LLM training schemes have incorporated both unidirectional and bidirectional objectives in their training now. However, this exploration hasn't been fully resolved as most models are still trained only on the causal objective by standard practice. There's still a a lot of exploration that can be done on pre-training objectives.
I'm having trouble understanding whether this paper is saying anything new. The original BERT paper already compared it favourably to causal models including GPT. Was there any doubt that BERT-style models could be in-context learners?
From what I gather as a non-expert, the problem with BERT is scaling/training efficiency: GPT gets C-1 training examples out of a training input of length C, but BERT only gets 0.15*C examples. Indeed, the author points out that DeBERTa required 3x more compute than GPT-3 to achieve the level of performance reported, which makes sense.
- Encoder based models have much faster inference (are auto-regressive) and are smaller. They are great for applications where speed and efficiency are key. - Most embedding models are BERT-based (see MTEB leaderboard). So widely used for retrieval. - They are also used to filter data for pre-training decoder models. The Llama 3 authors used a quality classifier (DistilRoberta) to generate quality scores for documents. Something similar is done for FineWeb Edu
So I’m trying BERT models out :)
- Generative model outputs are not always desirable, and often even undesirable
- BERT models are smaller and can run with lower latency and serve larger batches with lower vram requirements
- BERT models have bidirectional attention, which can improve performance in many applications
LLMs are “cheap” in the sense that they work well generically, without requiring fine tuning. Where they overlap with BERT models is mostly that they may work better in low training data environments due to better generalization capabilities.
But mostly companies like them because they don’t “require” ML engineers or data scientists on staff. For the lack of care given to evaluation that I see around LLM apps, I suspect that’s going to prove to be a faulty premise.
The most recent version of Wolfram Language (aka Mathematica) uses by default BERT models for embedding.
(Say, for this function: https://reference.wolfram.com/language/ref/CreateSemanticSea... .)
Also for analyzing Trump's tweets (from 2016): https://mathematicaforprediction.wordpress.com/2016/11/21/te...
But I believe we’re confident that all major models are causal transformer models right now.
No reason to believe otherwise. If one of them was doing something different, they’d let us know in order to stand out.